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Neuroinformatics challenges in MRI data integration

Neuroinformatics challenges in MRI data integration. Hugo Schnack Rudolf Magnus Institute of Neuroscience Department of Psychiatry University Medical Centre Utrecht www.smri.nl (h.schnack@umcutrecht.nl). Our recent acquisition: 7 Tesla scanner. Officially opened, December 4 th , 2007.

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Neuroinformatics challenges in MRI data integration

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  1. Neuroinformatics challenges in MRI data integration Hugo Schnack Rudolf Magnus Institute of Neuroscience Department of Psychiatry University Medical Centre Utrecht www.smri.nl(h.schnack@umcutrecht.nl)

  2. Our recent acquisition:7 Tesla scanner Officially opened, December 4th, 2007

  3. Our research Investigation of morphological brain abnormalities in psychiatric disorders (schizophrenia)Q: Are brains of schizophrenia patients smaller?Magnetic Resonance Imaging (MRI) scans of patients and healthy comparison subjectsImage processing and statistical analysesA: Yes, statistically they are.

  4. Schizophrenia patients have less gray matter than healthy subjects N=310 for this resultLarge variation in brain morphology

  5. Heritability of brain changes in schizophrenia

  6. Heritable brain changes in schizophrenia N=2x44 for this result(Discordant) twins are sparse P-CN-NP-CN-N

  7. We Need More Twins! International collaborations EUTwinsS (European Twin Study Network on Schizophrenia) Germany, the UK, The Netherlands, Spain, Hungary and Switzerland STAR (Schizophrenia Twin and Relatives) consortium Heidelberg, Jena, London, Utrecht, Helsinki

  8. Multicenter MRI Goal: combine MRI data from different scanners Can we combine brain scans from different scanners (machines, manufacturers, acquisition protocols, field strength, in time, …)?What do we mean by ‘Can’? (increase in power; closer to ‘the truth’ – can we know the truth?)Is there a measure of goodness for (processed) MRI scan?

  9. Truth Scanner Processing Derivatives: Segments, volumes, shapes, fiber tracts, …

  10. Truth Now Truth Two years later Another Scanner Scanner Now Scanner Two years later Processing (+2 yr) Derivatives: Segments, volumes, shapes, fiber tracts, … Derivatives: Segments, volumes, shapes, fiber tracts, …

  11. Multicenter MRI(STAR) STAR multicenter MRI calibration study: Schnack et al. 2004. Human Brain Mapping 22: 312-320.

  12. Multicenter MRI(STAR) STAR multicenter MRI calibration study: Schnack et al. 2004. Human Brain Mapping 22: 312-320.

  13. Multicenter MRI Calibration study Goal: combine MRI data from different scanners 6 healthy subjects scanned in Utrecht (twice), Heidelberg, Jena, London Processed with image processing pipeline in Utrecht 1. Measure reliability (fixed algorithms) 2. Calibrate algorithms (tunable parameters)

  14. Multicenter MRI ICC = true variation / (true variation + error) > 0.7 = “good” Reliability of tissue volumes (ICC) Gray White • Utrecht repeated 0.97 1.00 • Utrecht – London 0.94 0.99 • London – Jena 0.85 0.94 • Jena – Utrecht 0.88 0.94

  15. Voxelwise reliability (ICC) Utrecht repeated scans: 97% of the voxels has ICC > 0.7 (“good”)

  16. Multicenter MRI:Voxelwise reliability (ICC) Neff (gain factor)

  17. Challenges Comparability of MR images (scanners) Comparability of analysis tools (software) Comparability of their interactions Creation of gold standards (“truths”) Create better simulated MR images Other “calibration” mechanisms (instead of sending out 6 people around Europe?) How to present / summarize / visualize reliability? (generalizable?) Other modalities…

  18. ContributorsHugo SchnackNeeltje van HarenRachel BrouwerHilleke Hulshoff Pol (head Neuroimaging Psychiatry)René Kahn (head Dept. Psychiatry) Rudolf Magnus Institute of Neuroscience Department of Psychiatry University Medical Centre Utrecht www.smri.nl(h.schnack@umcutrecht.nl)

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